An artificial neural network (ANN) is a type of information processing system whose architecture is inspired by the biologically-evolved neural systems found in animals. A neural network includes an interconnected set of processing units called nodes. The network is designed to accept a set of input data, called input pattern data, process the input pattern data, and return a set of output data called output pattern data. Although dependent on other nodes in the network for input, each node in a neural network can be configured to independently process its input (e.g., in parallel) with other nodes of the network.
An ANN can be utilized in circumstances where a system receives multiple inputs, processes these inputs via nodes of the ANN, and performs actions based on outputs from the nodes. For example, a vehicle incorporating an ANN can 1) receive various inputs such as proximities to other vehicles, outside temperature, proximity to destination, etc., 2) process such inputs, and 3) modify the vehicle's behavior by, for example, adjusting the current speed of the vehicle, steering the vehicle, and/or changing the temperature inside the vehicle. ANNs may be used in various fields such as robotics, system identification and control (for example, vehicle control and process control), function approximation, regression analysis, pattern and sequence recognition (for example, radar systems, face detection, object detection, gesture recognition, speech recognition, and handwritten text recognition), novelty detection, sequential decision making (for example, chess, backgammon, and poker), computer numerical control, medical diagnosis, financial applications (for example, automated trading systems), data mining, e-mail spam filtering, other data processing, etc.